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  <div class="section" id="benchmark-performance-log-format">
<h1>Benchmark Performance Log Format<a class="headerlink" href="#benchmark-performance-log-format" title="永久链接至标题">¶</a></h1>
<p>This page details schema v0.1 for a unified benchmark log format. This schema will allow easier cross-references with other frameworks/runs, experiment reproduction, data for nightly perf regression, and the separation of logging/visualization efforts.</p>
<div class="section" id="log-format-overview">
<h2>Log Format Overview<a class="headerlink" href="#log-format-overview" title="永久链接至标题">¶</a></h2>
<p>For simplicity, we suggest prioritizing the fields <cite>workload</cite>, <cite>engine</cite>, <cite>hardware</cite> <cite>runtime_ms_mean</cite>, and <cite>runtime_ms_std</cite>. For finer-grained logging, one may additionally propagate the <cite>*_config</cite> fields.</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 8%" />
<col style="width: 60%" />
<col style="width: 5%" />
<col style="width: 27%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>header</p></th>
<th class="head"><p>examples</p></th>
<th class="head"><p>category</p></th>
<th class="head"><p>notes/justification</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>workload</p></td>
<td><p>resnet-18</p></td>
<td><p>workload</p></td>
<td><p>name of workload</p></td>
</tr>
<tr class="row-odd"><td><p>engine</p></td>
<td><p>“tvm” / “onnxruntime”</p></td>
<td><p>compiler</p></td>
<td></td>
</tr>
<tr class="row-even"><td><p>hardware</p></td>
<td><p>“gcp-c2-standard-16”</p></td>
<td><p>hardware</p></td>
<td><p>descriptor of target hardware environment</p></td>
</tr>
<tr class="row-odd"><td><p>runtime_ms_mean</p></td>
<td><p>12.452</p></td>
<td><p>statistics</p></td>
<td></td>
</tr>
<tr class="row-even"><td><p>runtime_ms_std</p></td>
<td><p>5.3</p></td>
<td><p>statistics</p></td>
<td></td>
</tr>
<tr class="row-odd"><td><p>timestamp</p></td>
<td><p>1572282699.6</p></td>
<td><p>metadata</p></td>
<td><p>indicates when this record is logged</p></td>
</tr>
<tr class="row-even"><td><p>schema_version</p></td>
<td><p>“0.1”</p></td>
<td><p>metadata</p></td>
<td><p>ensure reproducibility as we iterate on this schema</p></td>
</tr>
<tr class="row-odd"><td><p>metadata</p></td>
<td><p>{ “docker_tag”:”gcr.io/…/0a680”, … }</p></td>
<td><p>metadata</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">docker_tag</span></code> is optional</p></td>
</tr>
<tr class="row-even"><td><p>workload_args</p></td>
<td><p>{“input_name”: “Input3”, “input_shape”: [list_of_shape], “data_layout”: NHCW}</p></td>
<td><p>workload</p></td>
<td></td>
</tr>
<tr class="row-odd"><td><p>workload_metadata</p></td>
<td><p>{“class”: “vision”,”doc_url”: “<code class="docutils literal notranslate"><span class="pre">https://github.com/.../README.md</span></code>”, “opset”: 7,”type”: “body_analysis”,”url”: “<code class="docutils literal notranslate"><span class="pre">https://onnxzoo...ferplus.tar.gz</span></code>”, “md5”: “07fc7…”}</p></td>
<td><p>workload</p></td>
<td><p>source of workload</p></td>
</tr>
<tr class="row-even"><td><p>engine_version</p></td>
<td><p>“1.0.5”</p></td>
<td><p>compiler</p></td>
<td><p>use semvar format</p></td>
</tr>
<tr class="row-odd"><td><p>engine_config</p></td>
<td><p>{“llvm”: “llvm-8”, “nvcc”: 10.1, “accelerator”: “MLAS”, “relay_opt_level”: 3, “tvm_target”:”llvm -mcpu=cascadelake”}</p></td>
<td><p>compiler</p></td>
<td><p>fields are optionally specified</p></td>
</tr>
<tr class="row-even"><td><p>compilation_config</p></td>
<td><p>{“opt_level”: 3, “layer_schedules”:[]/ &lt;SHA_to_schedules&gt;}</p></td>
<td><p>compiler</p></td>
<td><p>fields are optionally specified</p></td>
</tr>
<tr class="row-odd"><td><p>software_config</p></td>
<td><p>{“os”: “ubuntu:18.04”,”pip”: { “docker”: “4.1.0”, “gitpython”: “3.0.4”, “numpy”: “1.17.4”, “onnx”: “1.6.0”}, “cudnn”: “cudnn-8”, “cuda_driver”: “480.10.1”}</p></td>
<td><p>backend</p></td>
<td><p>env dependency list</p></td>
</tr>
<tr class="row-even"><td><p>runtime_config</p></td>
<td><p>{“num_cpu_threads”: 3}</p></td>
<td><p>backend</p></td>
<td><p>info on non-hardware, non-software metadata</p></td>
</tr>
<tr class="row-odd"><td><p>hardware_config</p></td>
<td><p>{“cpu_count”: 16, “cloud_machine_type”:”c2-standard-16”, “memory_GB”:64}</p></td>
<td><p>hardware</p></td>
<td><p>json descriptor of target hardware environment</p></td>
</tr>
<tr class="row-even"><td><p>execution_config</p></td>
<td><p>{“number”: 1, “repeat”: 10, “min_repeat_ms”, 0}</p></td>
<td><p>statistics</p></td>
<td><p>workload execution parameters</p></td>
</tr>
<tr class="row-odd"><td><p>metrics</p></td>
<td><p>{“accuracy”: 48.5,“compilation_ms_mean”: 12}</p></td>
<td><p>statistics</p></td>
<td><p>other metrics</p></td>
</tr>
<tr class="row-even"><td><p>runtime_raw</p></td>
<td><p>[{“runtime_ms”: 12, …}, {“runtime_ms”:13,…},…]</p></td>
<td><p>statistics</p></td>
<td><p>optional raw metrics array</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="storage-format">
<h2>Storage format<a class="headerlink" href="#storage-format" title="永久链接至标题">¶</a></h2>
<p>Currently we’re prototyping benchmark data as JSON objects for extensibility and convenience, especially in early versions of the schema. However, as we scale up benchmark aggregation and stabilize parameters, we anticipate switching to a columnar format, such as Arrow or Parquet.</p>
<p>Here is sample data encoded as JSON:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span>
  <span class="s2">&quot;workload&quot;</span><span class="p">:</span><span class="s2">&quot;arcface_resnet100&quot;</span><span class="p">,</span>
  <span class="s2">&quot;engine&quot;</span><span class="p">:</span><span class="s2">&quot;tvm&quot;</span><span class="p">,</span>
  <span class="s2">&quot;hardware&quot;</span><span class="p">:</span><span class="s2">&quot;gcp-c2-standard-16&quot;</span><span class="p">,</span>
  <span class="s2">&quot;runtime_ms_mean&quot;</span><span class="p">:</span><span class="mf">109.43004820081924</span><span class="p">,</span>
  <span class="s2">&quot;runtime_ms_std&quot;</span><span class="p">:</span><span class="mf">0.09078385126800587</span><span class="p">,</span>
  <span class="s2">&quot;timestamp&quot;</span><span class="p">:</span><span class="s2">&quot;20191123003411&quot;</span><span class="p">,</span>
  <span class="s2">&quot;schema_version&quot;</span><span class="p">:</span><span class="s2">&quot;0.1&quot;</span><span class="p">,</span>
  <span class="s2">&quot;metadata&quot;</span><span class="p">:{</span>
    <span class="s2">&quot;docker_tag&quot;</span><span class="p">:</span><span class="s2">&quot;tlcpack/ci-gpu:v0.53&quot;</span>
  <span class="p">},</span>
  <span class="s2">&quot;workload_args&quot;</span><span class="p">:{</span>
    <span class="s2">&quot;input_shape_dict&quot;</span><span class="p">:{</span>
      <span class="s2">&quot;data&quot;</span><span class="p">:[</span>
        <span class="mi">1</span><span class="p">,</span>
        <span class="mi">3</span><span class="p">,</span>
        <span class="mi">112</span><span class="p">,</span>
        <span class="mi">112</span>
      <span class="p">]</span>
    <span class="p">},</span>
    <span class="s2">&quot;input_type_dict&quot;</span><span class="p">:{</span>
      <span class="s2">&quot;data&quot;</span><span class="p">:</span><span class="s2">&quot;float32&quot;</span>
    <span class="p">},</span>
    <span class="s2">&quot;input_value_dict&quot;</span><span class="p">:{}</span>
  <span class="p">},</span>
  <span class="s2">&quot;workload_metadata&quot;</span><span class="p">:{</span>
    <span class="s2">&quot;class&quot;</span><span class="p">:</span><span class="s2">&quot;vision&quot;</span><span class="p">,</span>
    <span class="s2">&quot;doc_url&quot;</span><span class="p">:</span><span class="s2">&quot;https://github.com/onnx/models/blob/master/vision/body_analysis/arcface/README.md&quot;</span><span class="p">,</span>
    <span class="s2">&quot;md5&quot;</span><span class="p">:</span><span class="s2">&quot;66074b860f905295aab5a842be57f37d&quot;</span><span class="p">,</span>
    <span class="s2">&quot;opset&quot;</span><span class="p">:</span><span class="mi">8</span><span class="p">,</span>
    <span class="s2">&quot;type&quot;</span><span class="p">:</span><span class="s2">&quot;body_analysis&quot;</span><span class="p">,</span>
    <span class="s2">&quot;url&quot;</span><span class="p">:</span><span class="s2">&quot;https://s3.amazonaws.com/onnx-model-zoo/arcface/resnet100/resnet100.tar.gz&quot;</span>
  <span class="p">},</span>
  <span class="s2">&quot;engine_version&quot;</span><span class="p">:</span><span class="s2">&quot;1.0.0&quot;</span><span class="p">,</span>
  <span class="s2">&quot;engine_config&quot;</span><span class="p">:{},</span>
  <span class="s2">&quot;compilation_config&quot;</span><span class="p">:{</span>
    <span class="s2">&quot;relay_opt_level&quot;</span><span class="p">:</span> <span class="mi">3</span>
  <span class="p">},</span>
  <span class="s2">&quot;software_config&quot;</span><span class="p">:{</span>
    <span class="s2">&quot;os&quot;</span><span class="p">:</span><span class="s2">&quot;ubuntu:18.04&quot;</span><span class="p">,</span>
    <span class="s2">&quot;pip&quot;</span><span class="p">:{</span>
      <span class="s2">&quot;docker&quot;</span><span class="p">:</span><span class="s2">&quot;4.1.0&quot;</span><span class="p">,</span>
      <span class="s2">&quot;gitpython&quot;</span><span class="p">:</span><span class="s2">&quot;3.0.4&quot;</span><span class="p">,</span>
      <span class="s2">&quot;numpy&quot;</span><span class="p">:</span><span class="s2">&quot;1.17.4&quot;</span><span class="p">,</span>
      <span class="s2">&quot;onnx&quot;</span><span class="p">:</span><span class="s2">&quot;1.6.0&quot;</span>
    <span class="p">}</span>
  <span class="p">},</span>
  <span class="s2">&quot;runtime_config&quot;</span><span class="p">:{},</span>
  <span class="s2">&quot;hardware_config&quot;</span><span class="p">:{</span>
    <span class="s2">&quot;cloud_machine_type&quot;</span><span class="p">:</span><span class="s2">&quot;c2-standard-16&quot;</span><span class="p">,</span>
    <span class="s2">&quot;cloud_provider&quot;</span><span class="p">:</span><span class="s2">&quot;GCP&quot;</span><span class="p">,</span>
    <span class="s2">&quot;cpu_count&quot;</span><span class="p">:</span><span class="mi">16</span><span class="p">,</span>
    <span class="s2">&quot;cpu_platform&quot;</span><span class="p">:</span><span class="s2">&quot;Intel Cascade Lake&quot;</span><span class="p">,</span>
    <span class="s2">&quot;memory_GB&quot;</span><span class="p">:</span><span class="mi">64</span>
  <span class="p">},</span>
  <span class="s2">&quot;execution_config&quot;</span><span class="p">:{},</span>
  <span class="s2">&quot;metrics&quot;</span><span class="p">:{}</span>
<span class="p">}</span>
</pre></div>
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